Pub Date : 2020-01-01DOI: 10.4018/IJOSSP.2020100102
Mahugnon Olivier Avande, R. Gandhi, Harvey P. Siy
License information for any non-trivial open-source software demonstrates the growing complexity of compliance management. Studies have shown that understanding open-source licenses is difficult. Prior research has not examined how developers would use interfaces displaying license text and its graphical models in studying a license. Consequently, a repeatable eye tracking-based methodology was developed to study user engagement when exploring open-source rights and obligations in a multi-modal fashion. Experiences of 10 participants in an exploratory case study design indicate that eye-tracking is feasible to quantitatively and qualitatively observe distinct interaction patterns in the use of license comprehension interfaces. A low correlation was observed between self-reported usability survey data and eye-tracking data. Conversely, a high correlation between eye-tracker and mouse data suggests the use of either in future studies. This paper provides a framework to conduct such studies as an alternative to surveys while offering interesting hypotheses for future studies.
{"title":"Understanding User Engagement With Multi-Representational License Comprehension Interfaces","authors":"Mahugnon Olivier Avande, R. Gandhi, Harvey P. Siy","doi":"10.4018/IJOSSP.2020100102","DOIUrl":"https://doi.org/10.4018/IJOSSP.2020100102","url":null,"abstract":"License information for any non-trivial open-source software demonstrates the growing complexity of compliance management. Studies have shown that understanding open-source licenses is difficult. Prior research has not examined how developers would use interfaces displaying license text and its graphical models in studying a license. Consequently, a repeatable eye tracking-based methodology was developed to study user engagement when exploring open-source rights and obligations in a multi-modal fashion. Experiences of 10 participants in an exploratory case study design indicate that eye-tracking is feasible to quantitatively and qualitatively observe distinct interaction patterns in the use of license comprehension interfaces. A low correlation was observed between self-reported usability survey data and eye-tracking data. Conversely, a high correlation between eye-tracker and mouse data suggests the use of either in future studies. This paper provides a framework to conduct such studies as an alternative to surveys while offering interesting hypotheses for future studies.","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"15 1","pages":"27-45"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78691128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.4018/IJOSSP.2020100103
Scaria Alex, T. Rajkumar
JavaScript is a scripting language that is commonly used in the web pages for providing dynamic functionality in order to enhance user experience. Malicious JavaScript in webpages on internet is an important security issue due to their potentially and universality severe impact. Finding the malicious JavaScript is usually more difficult and time-consuming task in the research community. Hence, an adaptive spider bird swarm algorithm-based deep recurrent neural network (adaptive SBSA-based deep RNN) is proposed for detecting the malicious JavaScript codes in web applications. However, the proposed adaptive SBSA is designed by integrating the adaptive concept with the bird swarm algorithm (BSA) and spider monkey optimization (SMO). With the deep RNN classifier, the complexity issues exists in detecting the malicious codes is effectively resolved through the process of hierarchical computation. Due to the efficiency of the proposed approach, it can evaluate under large real-life datasets.
JavaScript是一种脚本语言，通常用于网页中提供动态功能，以增强用户体验。恶意JavaScript由于其潜在的、普遍的、严重的影响而成为一个重要的网络安全问题。在研究社区中，查找恶意JavaScript通常是一项更加困难和耗时的任务。为此，提出了一种基于自适应蜘蛛鸟群算法的深度递归神经网络(adaptive SBSA-based deep RNN)来检测web应用程序中的恶意JavaScript代码。然而，本文提出的自适应SBSA是将自适应概念与鸟群算法(BSA)和蜘蛛猴优化(SMO)相结合而设计的。深度RNN分类器通过分层计算的过程，有效地解决了恶意代码检测中存在的复杂性问题。由于该方法的有效性，它可以在大型真实数据集下进行评估。
{"title":"Adaptive Spider Bird Swarm Algorithm-Based Deep Recurrent Neural Network for Malicious JavaScript Detection Using Box-Cox Transformation","authors":"Scaria Alex, T. Rajkumar","doi":"10.4018/IJOSSP.2020100103","DOIUrl":"https://doi.org/10.4018/IJOSSP.2020100103","url":null,"abstract":"JavaScript is a scripting language that is commonly used in the web pages for providing dynamic functionality in order to enhance user experience. Malicious JavaScript in webpages on internet is an important security issue due to their potentially and universality severe impact. Finding the malicious JavaScript is usually more difficult and time-consuming task in the research community. Hence, an adaptive spider bird swarm algorithm-based deep recurrent neural network (adaptive SBSA-based deep RNN) is proposed for detecting the malicious JavaScript codes in web applications. However, the proposed adaptive SBSA is designed by integrating the adaptive concept with the bird swarm algorithm (BSA) and spider monkey optimization (SMO). With the deep RNN classifier, the complexity issues exists in detecting the malicious codes is effectively resolved through the process of hierarchical computation. Due to the efficiency of the proposed approach, it can evaluate under large real-life datasets.","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"15 1","pages":"46-59"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90389808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.4018/ijossp.2020010104
Youcef Bouziane, M. Abdi, Salah Sadou
Public software repositories (SR) maintain a massive amount of valuable data offering opportunities to support software engineering (SE) tasks. Researchers have applied information retrieval techniques in mining software repositories. Topic models are one of these techniques. However, this technique does not give an interpretation nor labels to the extracted topics and it requires manual analysis to identify them. Some approaches were proposed to automatically label the topics using tags in SR, but they do not consider the existence of spam-tags and they have difficulties to scale to large tag space. This article introduces a novel approach called automatically labelled software topic model (AL-STM) that labels the topics based on observed tags in SR. It mitigates the shortcomings of manual and automatic labelling of topics in SE. AL-STM is implemented using 22K GitHub projects and evaluated in a SE task (tag recommending) against the currently used techniques. The empirical results suggest that AL-STM is more robust in terms of MAP and nDCG, and more scalable to large tag space.
{"title":"Automatically Labelled Software Topic Model","authors":"Youcef Bouziane, M. Abdi, Salah Sadou","doi":"10.4018/ijossp.2020010104","DOIUrl":"https://doi.org/10.4018/ijossp.2020010104","url":null,"abstract":"Public software repositories (SR) maintain a massive amount of valuable data offering opportunities to support software engineering (SE) tasks. Researchers have applied information retrieval techniques in mining software repositories. Topic models are one of these techniques. However, this technique does not give an interpretation nor labels to the extracted topics and it requires manual analysis to identify them. Some approaches were proposed to automatically label the topics using tags in SR, but they do not consider the existence of spam-tags and they have difficulties to scale to large tag space. This article introduces a novel approach called automatically labelled software topic model (AL-STM) that labels the topics based on observed tags in SR. It mitigates the shortcomings of manual and automatic labelling of topics in SE. AL-STM is implemented using 22K GitHub projects and evaluated in a SE task (tag recommending) against the currently used techniques. The empirical results suggest that AL-STM is more robust in terms of MAP and nDCG, and more scalable to large tag space.","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"25 1","pages":"57-78"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87149359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.4018/ijossp.2020010103
Anushree Agrawal, R. K. Singh
Changes are made frequently in software to incorporate new requirements. The changes made to one class are not limited to that particular class, but they also affect other entities. Early identification of these change prone entities is very essential for minimizing future faults in the software applications. Thus, it is very important to develop quality models for identifying the ripple effect of changed classes to effectively utilize the limited resources during the software development lifecycle. Association rule mining is a popular approach suggested in literature, but a major limitation of this approach is its inability to generate recommendations in case of new addition of classes. This article suggests the development of prediction model using learning techniques to overcome this limitation. The authors evaluate the performance of thirteen statistical, ML, and search-based techniques using eight open source software applications in this work. The findings of this study are promising and support the application of SBT and ML techniques for ripple effect identification.
{"title":"Ripple Effect Identification in Software Applications","authors":"Anushree Agrawal, R. K. Singh","doi":"10.4018/ijossp.2020010103","DOIUrl":"https://doi.org/10.4018/ijossp.2020010103","url":null,"abstract":"Changes are made frequently in software to incorporate new requirements. The changes made to one class are not limited to that particular class, but they also affect other entities. Early identification of these change prone entities is very essential for minimizing future faults in the software applications. Thus, it is very important to develop quality models for identifying the ripple effect of changed classes to effectively utilize the limited resources during the software development lifecycle. Association rule mining is a popular approach suggested in literature, but a major limitation of this approach is its inability to generate recommendations in case of new addition of classes. This article suggests the development of prediction model using learning techniques to overcome this limitation. The authors evaluate the performance of thirteen statistical, ML, and search-based techniques using eight open source software applications in this work. The findings of this study are promising and support the application of SBT and ML techniques for ripple effect identification.","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"11 1","pages":"41-56"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81150855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.4018/ijossp.2019100102
Jhansi Lakshmi Potharlanka, Maruthi Padmaja Turumella, R. Pote
Software quality can be improved by early software defect prediction models. However, class imbalance due to under representation of defects and the irrelevant metrics used to predict them are two major challenges that hinder the model performance. This article presents a new two-stage framework of Ensemble of Hybrid Feature selection (EHF) with Weighted Support Vector Machine Boosting (WSVMBoost), which further enhance the model performance. The EHF is the ensemble feature ranking of feature selection models such as filters and embedded models to select the relevant metrics. The classification ensembles, namely Random Forest, RUSBoost, WSVMBoost, and the base learners, namely Decision Tree, and SVM are also explored in this study using five software reliability datasets. From the statistical tests, EHF with WSVMBoost attained best mean rank in terms of performance than the rest of the feature selection hybrids in predicting the software defects. Additionally, this study has shown that both McCabe and Hasalted method level metrics are equally important in improving the model performance.
{"title":"A Study on Class Imbalancing Feature Selection and Ensembles on Software Reliability Prediction","authors":"Jhansi Lakshmi Potharlanka, Maruthi Padmaja Turumella, R. Pote","doi":"10.4018/ijossp.2019100102","DOIUrl":"https://doi.org/10.4018/ijossp.2019100102","url":null,"abstract":"Software quality can be improved by early software defect prediction models. However, class imbalance due to under representation of defects and the irrelevant metrics used to predict them are two major challenges that hinder the model performance. This article presents a new two-stage framework of Ensemble of Hybrid Feature selection (EHF) with Weighted Support Vector Machine Boosting (WSVMBoost), which further enhance the model performance. The EHF is the ensemble feature ranking of feature selection models such as filters and embedded models to select the relevant metrics. The classification ensembles, namely Random Forest, RUSBoost, WSVMBoost, and the base learners, namely Decision Tree, and SVM are also explored in this study using five software reliability datasets. From the statistical tests, EHF with WSVMBoost attained best mean rank in terms of performance than the rest of the feature selection hybrids in predicting the software defects. Additionally, this study has shown that both McCabe and Hasalted method level metrics are equally important in improving the model performance.","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"20 1","pages":"20-43"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90478417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.4018/ijossp.2019100101
Osama Al Qasem, Mohammed Akour
Software faults prediction (SFP) processes can be used for detecting faulty constructs at early stages of the development lifecycle, in addition to its being used in several phases of the development process. Machine learning (ML) is widely used in this area. One of the most promising subsets from ML is deep learning that achieves remarkable performance in various areas. Two deep learning algorithms are used in this paper, the Multi-layer perceptrons (MLPs) and Convolutional Neural Network (CNN). In order to evaluate the studied algorithms, four commonly used datasets from NASA are used i.e. (PC1, KC1, KC2 and CM1). The experiment results show how the CNN algorithm achieves prediction superiority of the MLP algorithm. The accuracy and detection rate measurements when using CNN has reached the standard ratio respectively as follows: PC1 97.7% - 73.9%, KC1 100% - 100%, KC2 99.3% - 99.2% and CM1 97.3% - 82.3%. This study provides promising results in using the deep learning for software fault prediction research.
{"title":"Software Fault Prediction Using Deep Learning Algorithms","authors":"Osama Al Qasem, Mohammed Akour","doi":"10.4018/ijossp.2019100101","DOIUrl":"https://doi.org/10.4018/ijossp.2019100101","url":null,"abstract":"Software faults prediction (SFP) processes can be used for detecting faulty constructs at early stages of the development lifecycle, in addition to its being used in several phases of the development process. Machine learning (ML) is widely used in this area. One of the most promising subsets from ML is deep learning that achieves remarkable performance in various areas. Two deep learning algorithms are used in this paper, the Multi-layer perceptrons (MLPs) and Convolutional Neural Network (CNN). In order to evaluate the studied algorithms, four commonly used datasets from NASA are used i.e. (PC1, KC1, KC2 and CM1). The experiment results show how the CNN algorithm achieves prediction superiority of the MLP algorithm. The accuracy and detection rate measurements when using CNN has reached the standard ratio respectively as follows: PC1 97.7% - 73.9%, KC1 100% - 100%, KC2 99.3% - 99.2% and CM1 97.3% - 82.3%. This study provides promising results in using the deep learning for software fault prediction research.","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"100 1","pages":"1-19"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85906330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.4018/ijossp.2019100103
G. Srinivasa, Amith K. Jain, Prithviraj Jain, R. NageshH.
Twitter is one among most popular micro blogging services with millions of active users. It is a hub of massive collection of data arriving from various sources. In Twitter, users most often express their views, opinions, thoughts, emotions or feelings about a particular topic, product or service, of their interest, choice or concern. This makes twitter a hub of gargantuan amount of data, and at the same time a useful platform in getting to know and understand the underlying sentiment behind a particular product or for that matter anything expressed in twitter as tweets. It is important to note here that aforesaid massive collection of data is not just any redundant data, but one which contains useful information as noted earlier. In view of aforesaid context, Sentiment analysis in relation to twitter data gains enormous importance. Sentiment analysis offers itself as a good approach in classifying the opinions formulated by individuals (tweeters) into different sentiments such as, positive, negative, or neutral. Implementing Sentiment analysis algorithms using conventional tools leads to high computation time, and thus are less effective. Hence, there is a need for state-of-the-art tools and techniques to be developed for sentiment analysis making it the need of the hour to facilitate faster computation. An Apache Hadoop framework is one such option that supports distributed data computing and has been commonly adopted for a variety of use-cases. In this article, the author identifies factors affecting the performance of sentiment analysis algorithms based on Hadoop framework and proposes an approach for optimizing the performance of sentiment analysis. The experimental results depict the potential of the proposed approach.
{"title":"A Novel Approach to Optimize the Performance of Hadoop Frameworks for Sentiment Analysis","authors":"G. Srinivasa, Amith K. Jain, Prithviraj Jain, R. NageshH.","doi":"10.4018/ijossp.2019100103","DOIUrl":"https://doi.org/10.4018/ijossp.2019100103","url":null,"abstract":"Twitter is one among most popular micro blogging services with millions of active users. It is a hub of massive collection of data arriving from various sources. In Twitter, users most often express their views, opinions, thoughts, emotions or feelings about a particular topic, product or service, of their interest, choice or concern. This makes twitter a hub of gargantuan amount of data, and at the same time a useful platform in getting to know and understand the underlying sentiment behind a particular product or for that matter anything expressed in twitter as tweets. It is important to note here that aforesaid massive collection of data is not just any redundant data, but one which contains useful information as noted earlier. In view of aforesaid context, Sentiment analysis in relation to twitter data gains enormous importance. Sentiment analysis offers itself as a good approach in classifying the opinions formulated by individuals (tweeters) into different sentiments such as, positive, negative, or neutral. Implementing Sentiment analysis algorithms using conventional tools leads to high computation time, and thus are less effective. Hence, there is a need for state-of-the-art tools and techniques to be developed for sentiment analysis making it the need of the hour to facilitate faster computation. An Apache Hadoop framework is one such option that supports distributed data computing and has been commonly adopted for a variety of use-cases. In this article, the author identifies factors affecting the performance of sentiment analysis algorithms based on Hadoop framework and proposes an approach for optimizing the performance of sentiment analysis. The experimental results depict the potential of the proposed approach.","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"44 1","pages":"44-59"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85344289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.4018/ijossp.2019070102
P. Ramgouda, V. Chandraprakash
The combinatorial strategy is useful in the reduction of the number of input parameters into a compact set of a system based on the combinations of the parameters. This strategy can be used in testing the behaviour that takes place when the events are allowed to be executed in an appropriate order. Basically, in the software systems, for the highly configurable system, the input configurations are based on the constraints, and the construction of this idea undergoes various kinds of difficulties. The proposed Jaya-Bat optimization algorithm is developed with the combinatorial interaction test cases in an effective manner in the presence of the constraints. The proposed Jaya-Bat based optimization algorithm is the integration of the Jaya optimization algorithm (JOA) and the Bat optimization algorithm (BA). The experimentation is carried out in terms of average size and the average time to prove the effectiveness of the proposed algorithm. From the results, it is clear that the proposed algorithm is capable of selecting the test cases optimally with better performance.
{"title":"Optimization Driven Constraints Handling in Combinatorial Interaction Testing","authors":"P. Ramgouda, V. Chandraprakash","doi":"10.4018/ijossp.2019070102","DOIUrl":"https://doi.org/10.4018/ijossp.2019070102","url":null,"abstract":"The combinatorial strategy is useful in the reduction of the number of input parameters into a compact set of a system based on the combinations of the parameters. This strategy can be used in testing the behaviour that takes place when the events are allowed to be executed in an appropriate order. Basically, in the software systems, for the highly configurable system, the input configurations are based on the constraints, and the construction of this idea undergoes various kinds of difficulties. The proposed Jaya-Bat optimization algorithm is developed with the combinatorial interaction test cases in an effective manner in the presence of the constraints. The proposed Jaya-Bat based optimization algorithm is the integration of the Jaya optimization algorithm (JOA) and the Bat optimization algorithm (BA). The experimentation is carried out in terms of average size and the average time to prove the effectiveness of the proposed algorithm. From the results, it is clear that the proposed algorithm is capable of selecting the test cases optimally with better performance.","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"46 3 1","pages":"19-37"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89575435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.4018/ijossp.2019070103
N. Alhindawi, B. A. Ata, Lana Obeidat, M. Al-Batah, M. Abu-Ata
In information retrieval, the accuracy of the retrieval process is mainly dependent on query terms selection; therefore, the user must choose the needed terms carefully and selectively. Traditionally, the process of selecting query terms is done manually. However, in the last two decades, a lot of research has been directed towards automating the process of choosing and enhancing query terms. In this article, a new novel approach is presented, which relies on topic modeling in query building and expansion. Two open source systems were selected to perform the experiments, results show that adding the topic's term to the user's query clearly improves its quality and thus, improves the ranking results.
{"title":"A Topic Modeling Based Approach for Enhancing Corpus Querying","authors":"N. Alhindawi, B. A. Ata, Lana Obeidat, M. Al-Batah, M. Abu-Ata","doi":"10.4018/ijossp.2019070103","DOIUrl":"https://doi.org/10.4018/ijossp.2019070103","url":null,"abstract":"In information retrieval, the accuracy of the retrieval process is mainly dependent on query terms selection; therefore, the user must choose the needed terms carefully and selectively. Traditionally, the process of selecting query terms is done manually. However, in the last two decades, a lot of research has been directed towards automating the process of choosing and enhancing query terms. In this article, a new novel approach is presented, which relies on topic modeling in query building and expansion. Two open source systems were selected to perform the experiments, results show that adding the topic's term to the user's query clearly improves its quality and thus, improves the ranking results.","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"294 1","pages":"38-50"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74987294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.4018/ijossp.2019070104
P. Aggarwal, P. S. Grover, Laxmi Ahuja
With the expansion in the quantity of cell phone utilization, mobile applications are developing significantly in today's high-tech environment. With this high demand, the quality of mobile applications is turning into a major issue. The organizations are still finding a way to develop quality applications. The number of quality models has already been proposed for assessing the quality of a mobile application but none of them provide a holistic view towards quality assurance. The present research work proposes an empirical evaluation of the SQM-MApp quality model using a hybrid multi-criteria decision-making approach named ELimination Et Choix Traduisant la REalité (ELimination and Choice Expressing REality) (ELECTRE-TRI) method and step-wise weight assessment ratio analysis (SWARA) method for ranking and determining weights of chosen quality factors respectively. The proposed approach specifically is for the mobile applications that are from the gaming domain. Also, validation of the proposed approach is performed by assessing the quality of gaming applications.
{"title":"Assessing Quality of Mobile Applications Based on a Hybrid MCDM Approach","authors":"P. Aggarwal, P. S. Grover, Laxmi Ahuja","doi":"10.4018/ijossp.2019070104","DOIUrl":"https://doi.org/10.4018/ijossp.2019070104","url":null,"abstract":"With the expansion in the quantity of cell phone utilization, mobile applications are developing significantly in today's high-tech environment. With this high demand, the quality of mobile applications is turning into a major issue. The organizations are still finding a way to develop quality applications. The number of quality models has already been proposed for assessing the quality of a mobile application but none of them provide a holistic view towards quality assurance. The present research work proposes an empirical evaluation of the SQM-MApp quality model using a hybrid multi-criteria decision-making approach named ELimination Et Choix Traduisant la REalité (ELimination and Choice Expressing REality) (ELECTRE-TRI) method and step-wise weight assessment ratio analysis (SWARA) method for ranking and determining weights of chosen quality factors respectively. The proposed approach specifically is for the mobile applications that are from the gaming domain. Also, validation of the proposed approach is performed by assessing the quality of gaming applications.","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"298 1","pages":"51-63"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79650923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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